NCF.ipynb

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      "name": "NCF.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hesamcs/AI/blob/main/NCF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "author hesam saki "
      ],
      "metadata": {
        "id": "HZmjsP2kTQEN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "7mNsyYTsYUFD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**read** data"
      ],
      "metadata": {
        "id": "E9nuf7vDYeT2"
      }
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        "outputId": "5efaa649-6f69-4f8c-f900-470404b2a361"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        },
        {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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      ],
      "source": [
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "path=\"/content/gdrive/MyDrive/Data_HZ/papers/neural collaborative filtering/impelement/ratings.dat\"\n",
        "dataframe=pd.read_csv(path)\n",
        "dataframe.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# data preprocessing \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "bl6sKeIeYwFS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "All ratings are contained in the file \"ratings.dat\" and are in the\n",
        "following format:\n",
        "\n",
        "UserID::MovieID::Rating::Timestamp\n",
        "\n",
        "- UserIDs range between 1 and 6040 \n",
        "- MovieIDs range between 1 and 3952\n",
        "- Ratings are made on a 5-star scale (whole-star ratings only)\n",
        "- Timestamp is represented in seconds since the epoch as returned by time(2)\n",
        "- Each user has at least 20 ratings\n",
        "\n",
        "😎"
      ],
      "metadata": {
        "id": "9NL_c3K0auhr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "set columns"
      ],
      "metadata": {
        "id": "h6qHwsY9iZj3"
      }
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    {
      "cell_type": "code",
      "source": [
        "dataframe=pd.read_csv(path,header=None,names=[\"user_id\",\"item_id\",\"rating\"],usecols=[0,1,2],sep=\"::\")\n",
        "print(\"unique User_ID is : \" , dataframe[\"user_id\"].unique())\n",
        "print(\"unique Item_ID is : \" , dataframe[\"item_id\"].unique())\n",
        "print(\"unique Rating is  : \" , dataframe[\"rating\"].unique())\n",
        "dataframe.head()"
      ],
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        "id": "u4fwRi2yY0y3",
        "colab": {
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  return func(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unique User_ID is :  [   1    2    3 ... 6038 6039 6040]\n",
            "unique Item_ID is :  [1193  661  914 ... 2845 3607 2909]\n",
            "unique Rating is  :  [5 3 4 2 1]\n"
          ]
        },
        {
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "   user_id  item_id  rating\n",
              "0        1     1193       5\n",
              "1        1      661       3\n",
              "2        1      914       3\n",
              "3        1     3408       4\n",
              "4        1     2355       5"
            ]
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        "dataframe=dataframe.sort_values([\"user_id\",\"item_id\"],ascending=True,na_position=\"first\" , ignore_index=True)\n",
        "dataframe"
      ],
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      "execution_count": null,
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              "<p>1000209 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-589dcbe3-86d5-441c-9e50-baa783f3b06f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-589dcbe3-86d5-441c-9e50-baa783f3b06f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-589dcbe3-86d5-441c-9e50-baa783f3b06f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         user_id  item_id  rating\n",
              "0              1        1       5\n",
              "1              1       48       5\n",
              "2              1      150       5\n",
              "3              1      260       4\n",
              "4              1      527       5\n",
              "...          ...      ...     ...\n",
              "1000204     6040     3683       4\n",
              "1000205     6040     3703       4\n",
              "1000206     6040     3735       4\n",
              "1000207     6040     3751       4\n",
              "1000208     6040     3819       5\n",
              "\n",
              "[1000209 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "statistics analysis "
      ],
      "metadata": {
        "id": "xWoTTk1KvzWU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.describe()"
      ],
      "metadata": {
        "id": "0kXQynHtuxQx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "82ff52b2-4446-4e1e-b03a-03d623185622"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7076367d-d32b-462e-bd17-ca6fc29d4446\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.000209e+06</td>\n",
              "      <td>1.000209e+06</td>\n",
              "      <td>1.000209e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.024512e+03</td>\n",
              "      <td>1.865540e+03</td>\n",
              "      <td>3.581564e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.728413e+03</td>\n",
              "      <td>1.096041e+03</td>\n",
              "      <td>1.117102e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.506000e+03</td>\n",
              "      <td>1.030000e+03</td>\n",
              "      <td>3.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.070000e+03</td>\n",
              "      <td>1.835000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.476000e+03</td>\n",
              "      <td>2.770000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.040000e+03</td>\n",
              "      <td>3.952000e+03</td>\n",
              "      <td>5.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7076367d-d32b-462e-bd17-ca6fc29d4446')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7076367d-d32b-462e-bd17-ca6fc29d4446 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7076367d-d32b-462e-bd17-ca6fc29d4446');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            user_id       item_id        rating\n",
              "count  1.000209e+06  1.000209e+06  1.000209e+06\n",
              "mean   3.024512e+03  1.865540e+03  3.581564e+00\n",
              "std    1.728413e+03  1.096041e+03  1.117102e+00\n",
              "min    1.000000e+00  1.000000e+00  1.000000e+00\n",
              "25%    1.506000e+03  1.030000e+03  3.000000e+00\n",
              "50%    3.070000e+03  1.835000e+03  4.000000e+00\n",
              "75%    4.476000e+03  2.770000e+03  4.000000e+00\n",
              "max    6.040000e+03  3.952000e+03  5.000000e+00"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "merge on movie title"
      ],
      "metadata": {
        "id": "JGJfrqdNwMgH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.read file"
      ],
      "metadata": {
        "id": "2QrySAmy0vBS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "path=\"/content/gdrive/MyDrive/Data_HZ/papers/neural collaborative filtering/impelement/movies.dat\"\n",
        "dataframe1=pd.read_csv(path , encoding='unicode_escape' , on_bad_lines='skip' , header=None , names=[\"item_id\",\"movie_name\",\"movie_geners\"] , sep=\"::\")\n",
        "dataframe1"
      ],
      "metadata": {
        "id": "M7FqELd6vMjT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "e70ed8ed-e09c-4ef8-b5d8-486bb4a7a179"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  return func(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-32165767-d162-4c45-8a50-581b10485271\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>movie_name</th>\n",
              "      <th>movie_geners</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Animation|Children's|Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children's|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3878</th>\n",
              "      <td>3948</td>\n",
              "      <td>Meet the Parents (2000)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3879</th>\n",
              "      <td>3949</td>\n",
              "      <td>Requiem for a Dream (2000)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3880</th>\n",
              "      <td>3950</td>\n",
              "      <td>Tigerland (2000)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3881</th>\n",
              "      <td>3951</td>\n",
              "      <td>Two Family House (2000)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3882</th>\n",
              "      <td>3952</td>\n",
              "      <td>Contender, The (2000)</td>\n",
              "      <td>Drama|Thriller</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3883 rows × 3 columns</p>\n",
              "</div>\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-32165767-d162-4c45-8a50-581b10485271 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-32165767-d162-4c45-8a50-581b10485271');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      item_id                          movie_name                  movie_geners\n",
              "0           1                    Toy Story (1995)   Animation|Children's|Comedy\n",
              "1           2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
              "2           3             Grumpier Old Men (1995)                Comedy|Romance\n",
              "3           4            Waiting to Exhale (1995)                  Comedy|Drama\n",
              "4           5  Father of the Bride Part II (1995)                        Comedy\n",
              "...       ...                                 ...                           ...\n",
              "3878     3948             Meet the Parents (2000)                        Comedy\n",
              "3879     3949          Requiem for a Dream (2000)                         Drama\n",
              "3880     3950                    Tigerland (2000)                         Drama\n",
              "3881     3951             Two Family House (2000)                         Drama\n",
              "3882     3952               Contender, The (2000)                Drama|Thriller\n",
              "\n",
              "[3883 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "merge"
      ],
      "metadata": {
        "id": "kInyfglM00s2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe2=pd.merge(dataframe,dataframe1,on=\"item_id\")\n",
        "dataframe2.head()"
      ],
      "metadata": {
        "id": "xbKRsjhK02Pk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "503b78e2-d2a1-4245-ad6a-e1f77e9f5de5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0acf934a-edfd-46df-9f2e-9f3ad084cc74\">\n",
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              "      <th>rating</th>\n",
              "      <th>movie_name</th>\n",
              "      <th>movie_geners</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Animation|Children's|Comedy</td>\n",
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              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
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              "      <td>Toy Story (1995)</td>\n",
              "      <td>Animation|Children's|Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Toy Story (1995)</td>\n",
              "      <td>Animation|Children's|Comedy</td>\n",
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              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Animation|Children's|Comedy</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Animation|Children's|Comedy</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0acf934a-edfd-46df-9f2e-9f3ad084cc74')\"\n",
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              "  </svg>\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0acf934a-edfd-46df-9f2e-9f3ad084cc74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   user_id  item_id  rating        movie_name                 movie_geners\n",
              "0        1        1       5  Toy Story (1995)  Animation|Children's|Comedy\n",
              "1        6        1       4  Toy Story (1995)  Animation|Children's|Comedy\n",
              "2        8        1       4  Toy Story (1995)  Animation|Children's|Comedy\n",
              "3        9        1       5  Toy Story (1995)  Animation|Children's|Comedy\n",
              "4       10        1       5  Toy Story (1995)  Animation|Children's|Comedy"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Favorite"
      ],
      "metadata": {
        "id": "ZR8LL49PWMbH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe2.describe()"
      ],
      "metadata": {
        "id": "-PQ-YNODWmr1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "5972e22a-5ce4-4ccf-aa65-ed0cd1be6ba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f9b5aa91-7ea0-4783-94cb-f99e71cae4f5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.000209e+06</td>\n",
              "      <td>1.000209e+06</td>\n",
              "      <td>1.000209e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.024512e+03</td>\n",
              "      <td>1.865540e+03</td>\n",
              "      <td>3.581564e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.728413e+03</td>\n",
              "      <td>1.096041e+03</td>\n",
              "      <td>1.117102e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.506000e+03</td>\n",
              "      <td>1.030000e+03</td>\n",
              "      <td>3.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.070000e+03</td>\n",
              "      <td>1.835000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.476000e+03</td>\n",
              "      <td>2.770000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.040000e+03</td>\n",
              "      <td>3.952000e+03</td>\n",
              "      <td>5.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9b5aa91-7ea0-4783-94cb-f99e71cae4f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f9b5aa91-7ea0-4783-94cb-f99e71cae4f5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f9b5aa91-7ea0-4783-94cb-f99e71cae4f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            user_id       item_id        rating\n",
              "count  1.000209e+06  1.000209e+06  1.000209e+06\n",
              "mean   3.024512e+03  1.865540e+03  3.581564e+00\n",
              "std    1.728413e+03  1.096041e+03  1.117102e+00\n",
              "min    1.000000e+00  1.000000e+00  1.000000e+00\n",
              "25%    1.506000e+03  1.030000e+03  3.000000e+00\n",
              "50%    3.070000e+03  1.835000e+03  4.000000e+00\n",
              "75%    4.476000e+03  2.770000e+03  4.000000e+00\n",
              "max    6.040000e+03  3.952000e+03  5.000000e+00"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "favorite=pd.DataFrame(dataframe2.groupby(\"movie_name\")[\"item_id\"].mean())\n",
        "favorite[\"number\"]=dataframe2.groupby(\"movie_name\")[\"item_id\"].count()\n",
        "favorite"
      ],
      "metadata": {
        "id": "AHD09C_AdVZz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "0d1baf14-18c7-4fbc-b4ed-b3b308dd640a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-02e4bc29-3b0d-435b-a875-4e672ecffa98\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movie_name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>$1,000,000 Duck (1971)</th>\n",
              "      <td>2031.0</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>'Night Mother (1986)</th>\n",
              "      <td>3112.0</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>'Til There Was You (1997)</th>\n",
              "      <td>779.0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>'burbs, The (1989)</th>\n",
              "      <td>2072.0</td>\n",
              "      <td>303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...And Justice for All (1979)</th>\n",
              "      <td>3420.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zed &amp; Two Noughts, A (1985)</th>\n",
              "      <td>3223.0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zero Effect (1998)</th>\n",
              "      <td>1845.0</td>\n",
              "      <td>301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zero Kelvin (Kjærlighetens kjøtere) (1995)</th>\n",
              "      <td>1364.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zeus and Roxanne (1997)</th>\n",
              "      <td>1426.0</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>eXistenZ (1999)</th>\n",
              "      <td>2600.0</td>\n",
              "      <td>410</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3706 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02e4bc29-3b0d-435b-a875-4e672ecffa98')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-02e4bc29-3b0d-435b-a875-4e672ecffa98');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                            item_id  number\n",
              "movie_name                                                 \n",
              "$1,000,000 Duck (1971)                       2031.0      37\n",
              "'Night Mother (1986)                         3112.0      70\n",
              "'Til There Was You (1997)                     779.0      52\n",
              "'burbs, The (1989)                           2072.0     303\n",
              "...And Justice for All (1979)                3420.0     199\n",
              "...                                             ...     ...\n",
              "Zed & Two Noughts, A (1985)                  3223.0      29\n",
              "Zero Effect (1998)                           1845.0     301\n",
              "Zero Kelvin (Kjærlighetens kjøtere) (1995)   1364.0       2\n",
              "Zeus and Roxanne (1997)                      1426.0      23\n",
              "eXistenZ (1999)                              2600.0     410\n",
              "\n",
              "[3706 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "max number for rating"
      ],
      "metadata": {
        "id": "dg5YQ0LYjs7W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "favorite=favorite.sort_values(\"number\",ascending=False)\n",
        "favorite"
      ],
      "metadata": {
        "id": "q6bl-hxIjsdz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "0ad1328b-f737-4933-e4d8-f95d5d736e14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movie_name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>American Beauty (1999)</th>\n",
              "      <td>2858.0</td>\n",
              "      <td>3428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
              "      <td>260.0</td>\n",
              "      <td>2991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Star Wars: Episode V - The Empire Strikes Back (1980)</th>\n",
              "      <td>1196.0</td>\n",
              "      <td>2990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Star Wars: Episode VI - Return of the Jedi (1983)</th>\n",
              "      <td>1210.0</td>\n",
              "      <td>2883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jurassic Park (1993)</th>\n",
              "      <td>480.0</td>\n",
              "      <td>2672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>Target (1995)</th>\n",
              "      <td>139.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>I Don't Want to Talk About It (De eso no se habla) (1993)</th>\n",
              "      <td>584.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>An Unforgettable Summer (1994)</th>\n",
              "      <td>790.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>Never Met Picasso (1996)</th>\n",
              "      <td>1714.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Full Speed (1996)</th>\n",
              "      <td>1724.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3706 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8623143c-2278-45c1-ae56-4b373edf12b4')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-8623143c-2278-45c1-ae56-4b373edf12b4');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "                                                    item_id  number\n",
              "movie_name                                                         \n",
              "American Beauty (1999)                               2858.0    3428\n",
              "Star Wars: Episode IV - A New Hope (1977)             260.0    2991\n",
              "Star Wars: Episode V - The Empire Strikes Back ...   1196.0    2990\n",
              "Star Wars: Episode VI - Return of the Jedi (1983)    1210.0    2883\n",
              "Jurassic Park (1993)                                  480.0    2672\n",
              "...                                                     ...     ...\n",
              "Target (1995)                                         139.0       1\n",
              "I Don't Want to Talk About It (De eso no se hab...    584.0       1\n",
              "An Unforgettable Summer (1994)                        790.0       1\n",
              "Never Met Picasso (1996)                             1714.0       1\n",
              "Full Speed (1996)                                    1724.0       1\n",
              "\n",
              "[3706 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "favorite1=pd.DataFrame(dataframe2.groupby(\"movie_geners\")[\"item_id\"].mean())\n",
        "favorite1[\"number\"]=dataframe2.groupby(\"movie_geners\")[\"item_id\"].count()\n",
        "favorite1"
      ],
      "metadata": {
        "id": "3AG5D1OZfS5K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "eceb7fa1-98df-4593-c1b7-ae3730d30de7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9b21a117-d9fc-41b8-975e-af2dc1505afd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>number</th>\n",
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              "    <tr>\n",
              "      <th>movie_geners</th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Action</th>\n",
              "      <td>2445.770855</td>\n",
              "      <td>12311</td>\n",
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              "    <tr>\n",
              "      <th>Action|Adventure</th>\n",
              "      <td>1433.903312</td>\n",
              "      <td>10446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Action|Adventure|Animation</th>\n",
              "      <td>3000.000000</td>\n",
              "      <td>345</td>\n",
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              "    <tr>\n",
              "      <th>Action|Adventure|Animation|Children's|Fantasy</th>\n",
              "      <td>558.000000</td>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Action|Adventure|Animation|Horror|Sci-Fi</th>\n",
              "      <td>610.000000</td>\n",
              "      <td>618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>Sci-Fi|Thriller|War</th>\n",
              "      <td>2287.000000</td>\n",
              "      <td>280</td>\n",
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              "    <tr>\n",
              "      <th>Sci-Fi|War</th>\n",
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              "      <td>1367</td>\n",
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              "    <tr>\n",
              "      <th>Thriller</th>\n",
              "      <td>2498.818946</td>\n",
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              "    <tr>\n",
              "      <th>War</th>\n",
              "      <td>2448.559031</td>\n",
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              "    <tr>\n",
              "      <th>Western</th>\n",
              "      <td>1962.151696</td>\n",
              "      <td>5689</td>\n",
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              "<p>301 rows × 2 columns</p>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9b21a117-d9fc-41b8-975e-af2dc1505afd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9b21a117-d9fc-41b8-975e-af2dc1505afd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                   item_id  number\n",
              "movie_geners                                                      \n",
              "Action                                         2445.770855   12311\n",
              "Action|Adventure                               1433.903312   10446\n",
              "Action|Adventure|Animation                     3000.000000     345\n",
              "Action|Adventure|Animation|Children's|Fantasy   558.000000     135\n",
              "Action|Adventure|Animation|Horror|Sci-Fi        610.000000     618\n",
              "...                                                    ...     ...\n",
              "Sci-Fi|Thriller|War                            2287.000000     280\n",
              "Sci-Fi|War                                      750.000000    1367\n",
              "Thriller                                       2498.818946   17851\n",
              "War                                            2448.559031     991\n",
              "Western                                        1962.151696    5689\n",
              "\n",
              "[301 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "max number for rating"
      ],
      "metadata": {
        "id": "GiB2WyCLkLs5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "favorite1=favorite.sort_values(\"number\",ascending=False)\n",
        "favorite1"
      ],
      "metadata": {
        "id": "VfBPtWB_kOQT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "1be28878-7330-41c5-d4a3-1dc8238eabca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-bbb03760-9b73-4020-b18b-b9484d7363ae\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movie_name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>American Beauty (1999)</th>\n",
              "      <td>2858.0</td>\n",
              "      <td>3428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
              "      <td>260.0</td>\n",
              "      <td>2991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Star Wars: Episode V - The Empire Strikes Back (1980)</th>\n",
              "      <td>1196.0</td>\n",
              "      <td>2990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Star Wars: Episode VI - Return of the Jedi (1983)</th>\n",
              "      <td>1210.0</td>\n",
              "      <td>2883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jurassic Park (1993)</th>\n",
              "      <td>480.0</td>\n",
              "      <td>2672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jar, The (Khomreh) (1992)</th>\n",
              "      <td>758.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Foolish (1999)</th>\n",
              "      <td>2584.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Slappy and the Stinkers (1998)</th>\n",
              "      <td>1843.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>For Ever Mozart (1996)</th>\n",
              "      <td>1579.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Full Speed (1996)</th>\n",
              "      <td>1724.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3706 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bbb03760-9b73-4020-b18b-b9484d7363ae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bbb03760-9b73-4020-b18b-b9484d7363ae button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bbb03760-9b73-4020-b18b-b9484d7363ae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                    item_id  number\n",
              "movie_name                                                         \n",
              "American Beauty (1999)                               2858.0    3428\n",
              "Star Wars: Episode IV - A New Hope (1977)             260.0    2991\n",
              "Star Wars: Episode V - The Empire Strikes Back ...   1196.0    2990\n",
              "Star Wars: Episode VI - Return of the Jedi (1983)    1210.0    2883\n",
              "Jurassic Park (1993)                                  480.0    2672\n",
              "...                                                     ...     ...\n",
              "Jar, The (Khomreh) (1992)                             758.0       1\n",
              "Foolish (1999)                                       2584.0       1\n",
              "Slappy and the Stinkers (1998)                       1843.0       1\n",
              "For Ever Mozart (1996)                               1579.0       1\n",
              "Full Speed (1996)                                    1724.0       1\n",
              "\n",
              "[3706 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **user_item**"
      ],
      "metadata": {
        "id": "FdUUrYnHioiQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "BVjDj4eoNXri"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_item_ui=dataframe2.pivot_table(index=\"user_id\", columns=\"movie_name\",values=\"rating\")\n",
        "user_item_ui"
      ],
      "metadata": {
        "id": "nI1u64j8iMmK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "3cc8240f-7d65-42d9-c4aa-9171fbc2d044"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-43e3478a-e23e-47ef-8656-c52c638b22fe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>movie_name</th>\n",
              "      <th>$1,000,000 Duck (1971)</th>\n",
              "      <th>'Night Mother (1986)</th>\n",
              "      <th>'Til There Was You (1997)</th>\n",
              "      <th>'burbs, The (1989)</th>\n",
              "      <th>...And Justice for All (1979)</th>\n",
              "      <th>1-900 (1994)</th>\n",
              "      <th>10 Things I Hate About You (1999)</th>\n",
              "      <th>101 Dalmatians (1961)</th>\n",
              "      <th>101 Dalmatians (1996)</th>\n",
              "      <th>12 Angry Men (1957)</th>\n",
              "      <th>13th Warrior, The (1999)</th>\n",
              "      <th>187 (1997)</th>\n",
              "      <th>2 Days in the Valley (1996)</th>\n",
              "      <th>20 Dates (1998)</th>\n",
              "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
              "      <th>200 Cigarettes (1999)</th>\n",
              "      <th>2001: A Space Odyssey (1968)</th>\n",
              "      <th>2010 (1984)</th>\n",
              "      <th>24 7: Twenty Four Seven (1997)</th>\n",
              "      <th>24-hour Woman (1998)</th>\n",
              "      <th>28 Days (2000)</th>\n",
              "      <th>3 Ninjas: High Noon On Mega Mountain (1998)</th>\n",
              "      <th>3 Strikes (2000)</th>\n",
              "      <th>301, 302 (1995)</th>\n",
              "      <th>39 Steps, The (1935)</th>\n",
              "      <th>400 Blows, The (Les Quatre cents coups) (1959)</th>\n",
              "      <th>42 Up (1998)</th>\n",
              "      <th>52 Pick-Up (1986)</th>\n",
              "      <th>54 (1998)</th>\n",
              "      <th>7th Voyage of Sinbad, The (1958)</th>\n",
              "      <th>8 1/2 (1963)</th>\n",
              "      <th>8 1/2 Women (1999)</th>\n",
              "      <th>8 Heads in a Duffel Bag (1997)</th>\n",
              "      <th>8 Seconds (1994)</th>\n",
              "      <th>8MM (1999)</th>\n",
              "      <th>A Chef in Love (1996)</th>\n",
              "      <th>Abbott and Costello Meet Frankenstein (1948)</th>\n",
              "      <th>Abominable Snowman, The (1957)</th>\n",
              "      <th>About Adam (2000)</th>\n",
              "      <th>About Last Night... (1986)</th>\n",
              "      <th>...</th>\n",
              "      <th>Wonderland (1997)</th>\n",
              "      <th>Wonderland (1999)</th>\n",
              "      <th>Woo (1998)</th>\n",
              "      <th>Wood, The (1999)</th>\n",
              "      <th>Wooden Man's Bride, The (Wu Kui) (1994)</th>\n",
              "      <th>Working Girl (1988)</th>\n",
              "      <th>World Is Not Enough, The (1999)</th>\n",
              "      <th>World of Apu, The (Apur Sansar) (1959)</th>\n",
              "      <th>Wrong Man, The (1956)</th>\n",
              "      <th>Wrong Trousers, The (1993)</th>\n",
              "      <th>Wrongfully Accused (1998)</th>\n",
              "      <th>Wyatt Earp (1994)</th>\n",
              "      <th>X-Files: Fight the Future, The (1998)</th>\n",
              "      <th>X-Men (2000)</th>\n",
              "      <th>X: The Unknown (1956)</th>\n",
              "      <th>Xiu Xiu: The Sent-Down Girl (Tian yu) (1998)</th>\n",
              "      <th>Yankee Zulu (1994)</th>\n",
              "      <th>Yards, The (1999)</th>\n",
              "      <th>Year My Voice Broke, The (1987)</th>\n",
              "      <th>Year of Living Dangerously (1982)</th>\n",
              "      <th>Year of the Horse (1997)</th>\n",
              "      <th>Yellow Submarine (1968)</th>\n",
              "      <th>Yojimbo (1961)</th>\n",
              "      <th>You Can't Take It With You (1938)</th>\n",
              "      <th>You So Crazy (1994)</th>\n",
              "      <th>You've Got Mail (1998)</th>\n",
              "      <th>Young Doctors in Love (1982)</th>\n",
              "      <th>Young Frankenstein (1974)</th>\n",
              "      <th>Young Guns (1988)</th>\n",
              "      <th>Young Guns II (1990)</th>\n",
              "      <th>Young Poisoner's Handbook, The (1995)</th>\n",
              "      <th>Young Sherlock Holmes (1985)</th>\n",
              "      <th>Young and Innocent (1937)</th>\n",
              "      <th>Your Friends and Neighbors (1998)</th>\n",
              "      <th>Zachariah (1971)</th>\n",
              "      <th>Zed &amp; Two Noughts, A (1985)</th>\n",
              "      <th>Zero Effect (1998)</th>\n",
              "      <th>Zero Kelvin (Kjærlighetens kjøtere) (1995)</th>\n",
              "      <th>Zeus and Roxanne (1997)</th>\n",
              "      <th>eXistenZ (1999)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6036</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6037</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6038</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6039</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6040</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6040 rows × 3706 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43e3478a-e23e-47ef-8656-c52c638b22fe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-43e3478a-e23e-47ef-8656-c52c638b22fe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-43e3478a-e23e-47ef-8656-c52c638b22fe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "movie_name  $1,000,000 Duck (1971)  ...  eXistenZ (1999)\n",
              "user_id                             ...                 \n",
              "1                              NaN  ...              NaN\n",
              "2                              NaN  ...              NaN\n",
              "3                              NaN  ...              NaN\n",
              "4                              NaN  ...              NaN\n",
              "5                              NaN  ...              NaN\n",
              "...                            ...  ...              ...\n",
              "6036                           NaN  ...              2.0\n",
              "6037                           NaN  ...              NaN\n",
              "6038                           NaN  ...              NaN\n",
              "6039                           NaN  ...              NaN\n",
              "6040                           NaN  ...              NaN\n",
              "\n",
              "[6040 rows x 3706 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "optimal"
      ],
      "metadata": {
        "id": "izC2kc1dmWky"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_item_ui=user_item_ui.fillna(0)\n",
        "user_item_ui\n"
      ],
      "metadata": {
        "id": "Md4JQodbmafa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "c169f715-963c-4eda-e3a0-84b417908be1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d48e991a-77e5-44af-952e-e4878c1b7a95\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>movie_name</th>\n",
              "      <th>$1,000,000 Duck (1971)</th>\n",
              "      <th>'Night Mother (1986)</th>\n",
              "      <th>'Til There Was You (1997)</th>\n",
              "      <th>'burbs, The (1989)</th>\n",
              "      <th>...And Justice for All (1979)</th>\n",
              "      <th>1-900 (1994)</th>\n",
              "      <th>10 Things I Hate About You (1999)</th>\n",
              "      <th>101 Dalmatians (1961)</th>\n",
              "      <th>101 Dalmatians (1996)</th>\n",
              "      <th>12 Angry Men (1957)</th>\n",
              "      <th>13th Warrior, The (1999)</th>\n",
              "      <th>187 (1997)</th>\n",
              "      <th>2 Days in the Valley (1996)</th>\n",
              "      <th>20 Dates (1998)</th>\n",
              "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
              "      <th>200 Cigarettes (1999)</th>\n",
              "      <th>2001: A Space Odyssey (1968)</th>\n",
              "      <th>2010 (1984)</th>\n",
              "      <th>24 7: Twenty Four Seven (1997)</th>\n",
              "      <th>24-hour Woman (1998)</th>\n",
              "      <th>28 Days (2000)</th>\n",
              "      <th>3 Ninjas: High Noon On Mega Mountain (1998)</th>\n",
              "      <th>3 Strikes (2000)</th>\n",
              "      <th>301, 302 (1995)</th>\n",
              "      <th>39 Steps, The (1935)</th>\n",
              "      <th>400 Blows, The (Les Quatre cents coups) (1959)</th>\n",
              "      <th>42 Up (1998)</th>\n",
              "      <th>52 Pick-Up (1986)</th>\n",
              "      <th>54 (1998)</th>\n",
              "      <th>7th Voyage of Sinbad, The (1958)</th>\n",
              "      <th>8 1/2 (1963)</th>\n",
              "      <th>8 1/2 Women (1999)</th>\n",
              "      <th>8 Heads in a Duffel Bag (1997)</th>\n",
              "      <th>8 Seconds (1994)</th>\n",
              "      <th>8MM (1999)</th>\n",
              "      <th>A Chef in Love (1996)</th>\n",
              "      <th>Abbott and Costello Meet Frankenstein (1948)</th>\n",
              "      <th>Abominable Snowman, The (1957)</th>\n",
              "      <th>About Adam (2000)</th>\n",
              "      <th>About Last Night... (1986)</th>\n",
              "      <th>...</th>\n",
              "      <th>Wonderland (1997)</th>\n",
              "      <th>Wonderland (1999)</th>\n",
              "      <th>Woo (1998)</th>\n",
              "      <th>Wood, The (1999)</th>\n",
              "      <th>Wooden Man's Bride, The (Wu Kui) (1994)</th>\n",
              "      <th>Working Girl (1988)</th>\n",
              "      <th>World Is Not Enough, The (1999)</th>\n",
              "      <th>World of Apu, The (Apur Sansar) (1959)</th>\n",
              "      <th>Wrong Man, The (1956)</th>\n",
              "      <th>Wrong Trousers, The (1993)</th>\n",
              "      <th>Wrongfully Accused (1998)</th>\n",
              "      <th>Wyatt Earp (1994)</th>\n",
              "      <th>X-Files: Fight the Future, The (1998)</th>\n",
              "      <th>X-Men (2000)</th>\n",
              "      <th>X: The Unknown (1956)</th>\n",
              "      <th>Xiu Xiu: The Sent-Down Girl (Tian yu) (1998)</th>\n",
              "      <th>Yankee Zulu (1994)</th>\n",
              "      <th>Yards, The (1999)</th>\n",
              "      <th>Year My Voice Broke, The (1987)</th>\n",
              "      <th>Year of Living Dangerously (1982)</th>\n",
              "      <th>Year of the Horse (1997)</th>\n",
              "      <th>Yellow Submarine (1968)</th>\n",
              "      <th>Yojimbo (1961)</th>\n",
              "      <th>You Can't Take It With You (1938)</th>\n",
              "      <th>You So Crazy (1994)</th>\n",
              "      <th>You've Got Mail (1998)</th>\n",
              "      <th>Young Doctors in Love (1982)</th>\n",
              "      <th>Young Frankenstein (1974)</th>\n",
              "      <th>Young Guns (1988)</th>\n",
              "      <th>Young Guns II (1990)</th>\n",
              "      <th>Young Poisoner's Handbook, The (1995)</th>\n",
              "      <th>Young Sherlock Holmes (1985)</th>\n",
              "      <th>Young and Innocent (1937)</th>\n",
              "      <th>Your Friends and Neighbors (1998)</th>\n",
              "      <th>Zachariah (1971)</th>\n",
              "      <th>Zed &amp; Two Noughts, A (1985)</th>\n",
              "      <th>Zero Effect (1998)</th>\n",
              "      <th>Zero Kelvin (Kjærlighetens kjøtere) (1995)</th>\n",
              "      <th>Zeus and Roxanne (1997)</th>\n",
              "      <th>eXistenZ (1999)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6036</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6037</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6038</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6039</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "  "
            ],
            "text/plain": [
              "movie_name  $1,000,000 Duck (1971)  ...  eXistenZ (1999)\n",
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              "...                            ...  ...              ...\n",
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              "6040                           0.0  ...              0.0\n",
              "\n",
              "[6040 rows x 3706 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1.generate zeros user_item matrix "
      ],
      "metadata": {
        "id": "Nd-OzzjxpKgi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mx_user=dataframe[\"user_id\"].max()+1\n",
        "mx_item=dataframe[\"item_id\"].max()+1\n",
        "user_item=np.zeros((mx_user,mx_item))"
      ],
      "metadata": {
        "id": "Q5Lp9Y6gj4ju"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2.generate user_item matrix"
      ],
      "metadata": {
        "id": "R3OzsfVFpWW0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(mx_user):\n",
        "   slice1=[]\n",
        "   slice1=dataframe[dataframe['user_id'] == i]\n",
        "   slice1=slice1[\"item_id\"]\n",
        "   slice1=slice1.values\n",
        "   for select_item in slice1:\n",
        "     user_item[i,select_item]=1\n",
        "  "
      ],
      "metadata": {
        "id": "iXIY4QCtrJxE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3.convert user_item matrix to DataFrame \n",
        "\n"
      ],
      "metadata": {
        "id": "LTviXYMT-eYH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_item=pd.DataFrame(data=user_item)\n",
        "user_item\n"
      ],
      "metadata": {
        "id": "tUpizyPptl14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "00732a37-0e11-42e1-dd5d-69f45760711a"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6036</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6037</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6038</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 17
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4.edit user_item DataFrame"
      ],
      "metadata": {
        "id": "JQNE5-k5J41V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_item=user_item.iloc[1:,1:]\n",
        "user_item"
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      "metadata": {
        "id": "1rQ0TcUfKDhH",
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          "height": 487
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        "outputId": "39fc282b-6e6f-46f6-f749-57abc7d86576"
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6036</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
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              "6038   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "6039   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "6040   1.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "\n",
              "[6040 rows x 3952 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Neural Collaborative Filtering"
      ],
      "metadata": {
        "id": "fG7Xt_MLKU1w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "2hNFW_jILgSN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[What is model and neurons in deep learning](https://playground.tensorflow.org/#activation=relu&regularization=L2&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0.01&noise=0&networkShape=4,4&seed=0.31815&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&activation_hide=false) "
      ],
      "metadata": {
        "id": "BF-wClN195qQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1.import library for NCF"
      ],
      "metadata": {
        "id": "uHk8k5-OLzAf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow.keras as keras\n",
        "from tensorflow.keras.layers import (\n",
        "    Concatenate,\n",
        "    Dense,\n",
        "    Embedding,\n",
        "    Flatten,\n",
        "    Input,\n",
        "    Multiply,\n",
        ")\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from typing import List"
      ],
      "metadata": {
        "id": "5dO7o1UwKk7c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2.initial NCF"
      ],
      "metadata": {
        "id": "m_ovS2DbP20i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def NCF( number_of_users: int,number_of_items: int,latent_dim_mf: int = 4,latent_dim_mlp: int = 32,reg_mf: int = 0,reg_mlp: int = 0.01,dense_layers: List[int] = [12, 8],reg_layers: List[int] = [0.01, 0.01],activation_dense: str = \"relu\",) -> keras.Model:\n",
        "\n",
        "    # input layer\n",
        "    user = Input(shape=(), dtype=\"int32\", name=\"user_id\")\n",
        "    item = Input(shape=(), dtype=\"int32\", name=\"item_id\")\n",
        "\n",
        "    # embedding layers\n",
        "    mf_user_embedding = Embedding(\n",
        "        input_dim=number_of_users,\n",
        "        output_dim=latent_dim_mf,\n",
        "        name=\"mf_user_embedding\",\n",
        "        embeddings_initializer=\"RandomNormal\",\n",
        "        embeddings_regularizer=l2(reg_mf),\n",
        "        input_length=1,\n",
        "    )\n",
        "    mf_item_embedding = Embedding(\n",
        "        input_dim=number_of_items,\n",
        "        output_dim=latent_dim_mf,\n",
        "        name=\"mf_item_embedding\",\n",
        "        embeddings_initializer=\"RandomNormal\",\n",
        "        embeddings_regularizer=l2(reg_mf),\n",
        "        input_length=1,\n",
        "    )\n",
        "\n",
        "    mlp_user_embedding = Embedding(\n",
        "        input_dim=number_of_users,\n",
        "        output_dim=latent_dim_mlp,\n",
        "        name=\"mlp_user_embedding\",\n",
        "        embeddings_initializer=\"RandomNormal\",\n",
        "        embeddings_regularizer=l2(reg_mlp),\n",
        "        input_length=1,\n",
        "    )\n",
        "    mlp_item_embedding = Embedding(\n",
        "        input_dim=number_of_items,\n",
        "        output_dim=latent_dim_mlp,\n",
        "        name=\"mlp_item_embedding\",\n",
        "        embeddings_initializer=\"RandomNormal\",\n",
        "        embeddings_regularizer=l2(reg_mlp),\n",
        "        input_length=1,\n",
        "    )\n",
        "\n",
        "    # MF vector\n",
        "    mf_user_latent = Flatten()(mf_user_embedding(user))\n",
        "    mf_item_latent = Flatten()(mf_item_embedding(item))\n",
        "    mf_cat_latent = Multiply()([mf_user_latent, mf_item_latent])\n",
        "\n",
        "    # MLP vector\n",
        "    mlp_user_latent = Flatten()(mlp_user_embedding(user))\n",
        "    mlp_item_latent = Flatten()(mlp_item_embedding(item))\n",
        "    mlp_cat_latent = Concatenate()([mlp_user_latent, mlp_item_latent])\n",
        "\n",
        "    mlp_vector = mlp_cat_latent\n",
        "\n",
        "    # build dense layers for model\n",
        "    for i in range(len(dense_layers)):\n",
        "        layer = Dense(\n",
        "            dense_layers[i],\n",
        "            activity_regularizer=l2(reg_layers[i]),\n",
        "            activation=activation_dense,\n",
        "            name=\"layer%d\" % i,\n",
        "        )\n",
        "        mlp_vector = layer(mlp_vector)\n",
        "\n",
        "    predict_layer = Concatenate()([mf_cat_latent, mlp_vector])\n",
        "\n",
        "    result = Dense(\n",
        "        1, activation=\"sigmoid\", kernel_initializer=\"lecun_uniform\", name=\"interaction\"\n",
        "    )\n",
        "\n",
        "    output = result(predict_layer)\n",
        "\n",
        "    model = Model(\n",
        "        inputs=[user, item],\n",
        "        outputs=[output],\n",
        "    )\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "TVoDPmd7QaxF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##train test"
      ],
      "metadata": {
        "id": "XMq5yzn36KgI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe3=dataframe"
      ],
      "metadata": {
        "id": "jwpnpl9VYWn5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe3=dataframe[\"rating\"]>=1\n",
        "dataframe3 = dataframe3.astype(int)\n",
        "dataframe3"
      ],
      "metadata": {
        "id": "166ZSBAadkvG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64cc0f4b-22ba-47ad-debd-31c12d1e56d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          1\n",
              "1          1\n",
              "2          1\n",
              "3          1\n",
              "4          1\n",
              "          ..\n",
              "1000204    1\n",
              "1000205    1\n",
              "1000206    1\n",
              "1000207    1\n",
              "1000208    1\n",
              "Name: rating, Length: 1000209, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe[\"rating\"]=dataframe3\n",
        "dataframe"
      ],
      "metadata": {
        "id": "9SZAqKtdeIM4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "3fd95435-f78b-4e81-e1d2-36d26d26d93d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>260</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>527</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>1000204</th>\n",
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              "    <tr>\n",
              "      <th>1000205</th>\n",
              "      <td>6040</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1000206</th>\n",
              "      <td>6040</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1000207</th>\n",
              "      <td>6040</td>\n",
              "      <td>3751</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1000208</th>\n",
              "      <td>6040</td>\n",
              "      <td>3819</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000209 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5266450-3232-4aa5-96fc-45f13d5aa4e2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f5266450-3232-4aa5-96fc-45f13d5aa4e2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f5266450-3232-4aa5-96fc-45f13d5aa4e2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         user_id  item_id  rating\n",
              "0              1        1       1\n",
              "1              1       48       1\n",
              "2              1      150       1\n",
              "3              1      260       1\n",
              "4              1      527       1\n",
              "...          ...      ...     ...\n",
              "1000204     6040     3683       1\n",
              "1000205     6040     3703       1\n",
              "1000206     6040     3735       1\n",
              "1000207     6040     3751       1\n",
              "1000208     6040     3819       1\n",
              "\n",
              "[1000209 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "array=dataframe.values\n",
        "predict=array[:,2]\n",
        "train=array[:,:2]\n",
        "predict"
      ],
      "metadata": {
        "id": "zFMBS43CWF6j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a51f899-584a-431c-d4d9-30e73f907d3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "compile"
      ],
      "metadata": {
        "id": "RfRMXozkoq5D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "import tensorflow as tf\n",
        "\n",
        "ncfmodel = NCF(mx_user, mx_item)\n",
        "\n",
        "ncfmodel.compile(\n",
        "    optimizer=Adam(),\n",
        "    loss=\"binary_crossentropy\",\n",
        "    metrics=[\"accuracy\"]\n",
        ")\n",
        "ncfmodel.summary()"
      ],
      "metadata": {
        "id": "iNIX1QR_YIZo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ecb01de-0667-4f0d-f524-0f3ecb280454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " user_id (InputLayer)           [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " item_id (InputLayer)           [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " mlp_user_embedding (Embedding)  (None, 32)          193312      ['user_id[0][0]']                \n",
            "                                                                                                  \n",
            " mlp_item_embedding (Embedding)  (None, 32)          126496      ['item_id[0][0]']                \n",
            "                                                                                                  \n",
            " flatten_2 (Flatten)            (None, 32)           0           ['mlp_user_embedding[0][0]']     \n",
            "                                                                                                  \n",
            " flatten_3 (Flatten)            (None, 32)           0           ['mlp_item_embedding[0][0]']     \n",
            "                                                                                                  \n",
            " mf_user_embedding (Embedding)  (None, 4)            24164       ['user_id[0][0]']                \n",
            "                                                                                                  \n",
            " mf_item_embedding (Embedding)  (None, 4)            15812       ['item_id[0][0]']                \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 64)           0           ['flatten_2[0][0]',              \n",
            "                                                                  'flatten_3[0][0]']              \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 4)            0           ['mf_user_embedding[0][0]']      \n",
            "                                                                                                  \n",
            " flatten_1 (Flatten)            (None, 4)            0           ['mf_item_embedding[0][0]']      \n",
            "                                                                                                  \n",
            " layer0 (Dense)                 (None, 12)           780         ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " multiply (Multiply)            (None, 4)            0           ['flatten[0][0]',                \n",
            "                                                                  'flatten_1[0][0]']              \n",
            "                                                                                                  \n",
            " layer1 (Dense)                 (None, 8)            104         ['layer0[0][0]']                 \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate)    (None, 12)           0           ['multiply[0][0]',               \n",
            "                                                                  'layer1[0][0]']                 \n",
            "                                                                                                  \n",
            " interaction (Dense)            (None, 1)            13          ['concatenate_1[0][0]']          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 360,681\n",
            "Trainable params: 360,681\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "ppjtAj05VVgP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a763b6a-bc0e-4ddb-b6b3-02772085851f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    }
  ]
}